CS825: Information VisualizationHomework #1

ByAhmed H. Salem

UIN: 00940407

Written Assignment1. Describe the linkages between the stages of the visualization pipeline

and Fry's seven stages of data visualization.

Visualization pipeline consist of four stages that includes some looping till a satisfactory visualization is acquired. It consists of Data modeling, selection, visual mapping, parameter setting, and generating the visualization. Data modeling: putting the data in a format that is readable by the visualization tool. Data selection: based on a specific criteria select a subset of the data to be visualized. Visual mapping: is using the formulated data to generate visual representation. Parameter setting: adjusting the visualization parameters as colors, sizes, and location to give more emphasis on the message delivered. Rendering: is the last stage in the visualization pipeline, which is simply responsible for plotting the results of the previous phases. If the results were not satisfactory, it is possible to jump back to the required stage and perform the modifications until the visualization is clear enough.

Fry’s visualization pipelining consists of the following seven stages: acquire, parse, filter, mine, represent, refine, and interact. Acquire: collect the data and put it in one file. Filter: eliminate the unneeded data. Mine: apply statistical analysis on the dataset. Represent: choose a visual model. Refine: enhance the results from the representation phase. Interact: play with the features giving some emphasis on some of them.

In the previous text it is clear that both pipelines have nearly the same steps and results but with different terminology. The Data modeling the visualization pipeline can be mapped to acquire and parse stages in the Fry’s model. Data selection is mapped to filtering and mining. Visual mapping to representation, Visual setting to refine, and finally rendering to interact.

2. Give an example of each of the three categories of visualization: presentation, confirmation, exploration. Provide an image and URL for each example and explain how it fits into the category

The visualization can be used mainly for three purposes: exploration, confirmation, and presentation. Exploration: where the dataset is still new and the goal is to search for available features. Confirmation: specific features are found in the dataset and the visualization is meant to give more emphasis on them. Presentation: trying to convince others with the features in the dataset.

Exploration:The following visualization represents the geographical distribution of cellular antennas over Ivory cost, Africa in 2011. The goal of this research was to see how the cellar network could be enhanced with lower infrastructure and running costs. The visualization represents the direct plotting of the raw data (latitude and longitude) for the antennas over the geographical map to be used as the initial step for the network enhancement.

Link: D4D book: http://perso.uclouvain.be/vincent.blondel/netmob/2013/

Confirmation:

The following visualization confirms the results that apple usage is more popular than android. The study was meant to understand the usage of smartphone phone devices among Americans especially moms. The results in the visualization concentrates on only two results confirming the hypothesis that iOS is more popular than Android. Link: http://blog.flurry.com/?Tag=Uspagep%20Statistics

Presentation:

SticknFind is a Bluetooth based product using a Bluetooth tag and a smartphone to locate specific objects. The following visualization shows users the features in the product conveying them to buy it. Therefore in my opinion it can be considered presentation visualization.

Link: https://www.sticknfind.com/?utm_expid=68309787-0.eWK65GqZQ0uHBdpdinfJNw.0&utm_referrer=https%3A%2F%2Fwww.google.com%2F

3. Look at the papers from InfoVis 2012 and skim a paper you find interesting. In just a few paragraphs, discuss the motivation for the work, the basic idea, and what potential it has to aid human understanding. Include an image or two if available.

Title: Visualizing Student Histories Using Clustering and CompositionReference: Trimm, David, Penny Rheingans, and Marie desJardins. "Visualizing Student

Histories Using Clustering and Composition." Visualization and Computer Graphics, IEEE Transactions on 18.12 (2012): 2809-2818.

This paper’s goal is to visualize the academic history for students. The dataset used is collected from various academic levels. It was though of the student course history as independent instances distributed among semesters. The goal of this study is to connect these instances giving a closer look on how students behave towards their courses.The challenge in this work appears from the nature of courses distribution over the time period. Some courses are taken concurrently while others in different semesters. The overall results of these courses will count towards the student’s GPA. The study will try to highlight who courses affect their successors and how the students skills are progressed during his study period.

The previous Figure shows the plotting of all the student results among various semesters (exploration visualization). As the figure represents all the collected data at once even with adding the colors as a perspective in the presented graph it is very hard to extract any useful piece of information out of it. In the rest of the paper the authors analyzed the results more to get a close insight.

The previous figure shows how the results of different students groups towards their elementary courses (e.g. Column 1: A students, Column 2 : B students, etc…). The colors in the graphs indicated the grade as the grade scale from 2 to 4 the colors are from red to green. The results showed that A students (column 1) had no problems in both courses. B students grades earned in the prerequisite course (row 1) had a great effect on the successor course. For C students (column 3) it was shown that their unsatisfactory results in the elementary course gave them an incentive in the successor one.

The presented results show the importance of visualization techniques towards the clarity and analysis of complicated datasets.